Indians Scorecard

This appears to be a text file containing the game results for Major League Baseball (MLB) games. Each line represents a single game result in the format:

[Home Team] [Score] [Away Team]

Here's a reformatted version of the data with some additional information extracted from each line, such as the date and stadium:

{
  "games": [
    {
      "date": "June 17, 2022",
      "home_team": "Cleveland Indians",
      "score": "7-3",
      "away_team": "Kansas City Royals"
    },
    ...
  ]
}

Note that I've used some sample data to generate this example. The actual data would require more processing to extract the necessary information.

Here is a Python script using regular expressions and the pandas library to parse and clean the text file:

import re
import pandas as pd

def parse_game_result(line):
    match = re.match(r"(\w+ \w+) (\d+)-(\d+), (\w+) (\w+)", line)
    if match:
        return {
            "date": None,
            "home_team": match.group(1),
            "score": f"{match.group(2)}-{match.group(3)}",
            "away_team": match.group(4),
        }
    else:
        return None

def main():
    with open("game_results.txt", "r") as file:
        lines = file.readlines()
        data = [parse_game_result(line) for line in lines if parse_game_result(line)]
    
    df = pd.DataFrame(data)
    print(df)

if __name__ == "__main__":
    main()

This script assumes that the text file is named "game_results.txt" and is located in the same directory as the script. The parse_game_result function uses regular expressions to extract the necessary information from each line, and the main function reads the file, parses each line, and creates a pandas DataFrame from the parsed data.

Please note that you'll need to adjust the path to the text file if it's located elsewhere. Updated: July 18, 2025 at 4:17 AM